# Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

arXiv: https://arxiv.org/abs/2112.10107 · HTML: https://mlsanity.com/paper/2112.10107/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [Efficient Pressure: Improving efficiency for signalized intersections](https://mlsanity.com/paper/2112.02336/) (2021)
- [Decentralized Signal Control for Urban Road Networks](https://mlsanity.com/paper/1310.0491/) (2013)
- [Capacity-aware back-pressure traffic signal control](https://mlsanity.com/paper/1309.6484/) (2013)
- [Back-pressure traffic signal control with unknown routing rates](https://mlsanity.com/paper/1401.3357/) (2013)

## Built on by (most cited first, 29 of 29)

- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control](https://mlsanity.com/paper/2312.05090/) (2023)
- [CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation](https://mlsanity.com/paper/2210.00896/) (2022)
- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data](https://mlsanity.com/paper/2410.22938/) (2024)
- [Goal-Conditioned Data Augmentation for Offline Reinforcement Learning](https://mlsanity.com/paper/2412.20519/) (2024)
- [HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2304.03697/) (2023)
- [Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach](https://mlsanity.com/paper/2312.07795/) (2023)
- [Learning Traffic Signal Control via Genetic Programming](https://mlsanity.com/paper/2403.17328/) (2024)
- [GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy](https://mlsanity.com/paper/2508.16090/) (2025)
- [DataLight: Offline Data-Driven Traffic Signal Control](https://mlsanity.com/paper/2303.10828/) (2023)
- [Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2503.11488/) (2025)
- [Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2503.20205/) (2025)
- [A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control](https://mlsanity.com/paper/2511.00136/) (2025)
- [SymLight: Exploring Interpretable and Deployable Symbolic Policies for Traffic Signal Control](https://mlsanity.com/paper/2511.05790/) (2025)
- [CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2603.24366/) (2026)
- [DynamicLight: Two-Stage Dynamic Traffic Signal Timing](https://mlsanity.com/paper/2211.01025/) (2022)
- [Evolutionary Discovery of Heuristic Policies for Traffic Signal Control](https://mlsanity.com/paper/2511.23122/) (2025)
- [LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2603.24361/) (2026)
- [A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking](https://mlsanity.com/paper/2406.10661/) (2024)
- [Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse](https://mlsanity.com/paper/2503.08728/) (2025)
- [A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control](https://mlsanity.com/paper/2503.14250/) (2025)
- [CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning](https://mlsanity.com/paper/2512.09368/) (2025)
- [Spatiotemporal Decision Transformer for Traffic Coordination](https://mlsanity.com/paper/2602.02903/) (2026)
- [Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learning](https://mlsanity.com/paper/2602.04821/) (2026)
- [CROSS: A Mixture-of-Experts Reinforcement Learning Framework for Generalizable Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2603.24930/) (2026)
- [CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control](https://mlsanity.com/paper/2604.05663/) (2026)
- [C2T: Captioning-Structure and LLM-Aligned Common-Sense Reward Learning for Traffic--Vehicle Coordination](https://mlsanity.com/paper/2604.13098/) (2026)
- [VLALight: A Vision-Language-Action Model for Traffic Signal Control](https://mlsanity.com/paper/2609.36934/) (2026)

## Abstract (arXiv)

Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, we (1) present a novel, flexible and efficient method, namely advanced max pressure (Advanced-MP), taking both running and queuing vehicles into consideration to decide whether to change current signal phase; (2) inventively design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); and (3) develop a reinforcement learning (RL) based algorithm template, called Advanced-XLight, by combining ATS with the latest RL approaches, and generate two RL algorithms, namely "Advanced-MPLight" and "Advanced-CoLight" from Advanced-XLight. Comprehensive experiments on multiple real-world datasets show that: (1) the Advanced-MP outperforms baseline methods, and it is also efficient and reliable for deployment; and (2) Advanced-MPLight and Advanced-CoLight can achieve the state-of-the-art.